library(dyngen)This vignette demonstrates the different dynamic processes topologies (e.g. bifurcating and cyclic). If you haven’t done so already, first check out the installation instructions in the README.
You can find a full list of backbones using ?list_backbones. This vignette will showcase each of them individually.
set.seed(1)
backbone <- backbone_linear()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(2)
backbone <- backbone_bifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(3)
backbone <- backbone_bifurcating_converging()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(4)
backbone <- backbone_bifurcating_cycle()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(5)
backbone <- backbone_bifurcating_loop()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(6)
backbone <- backbone_binary_tree(
num_modifications = 2
)
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(7)
backbone <- backbone_branching(
num_modifications = 2,
min_degree = 3,
max_degree = 3
)
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
out$plotset.seed(8)
backbone <- backbone_consecutive_bifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
out$plotset.seed(9)
backbone <- backbone_trifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(10)
backbone <- backbone_converging()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(11)
backbone <- backbone_cycle()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(12)
backbone <- backbone_disconnected()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
out$plotset.seed(13)
backbone <- backbone_linear_simple()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plotset.seed(14)
backbone <- backbone_cycle_simple()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot